Semi-supervised learning approach for crack detection and identification in advanced gas-cooled reactor graphite bricks

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)
9 Downloads (Pure)

Abstract

One of the life-limiting components of an Advanced Gas cooled Reactor (AGR) is its graphite core. The bricks present in the core undergo radiolytic oxidation throughout their lifetime which causes graphite weight loss and irradiation which can result in some of the bricks developing cracks. Understanding the nature and extent of brick cracking within the core is key to ensuring continued and extended operation of the AGR fleet. A semi-supervised machine learning classification algorithm is proposed as a method for improving the detection of cracked graphite bricks, by combining the labels derived from infrequent, detailed inspections of the core, with unlabeled, more frequent monitoring measurements taken during refueling operations. Semi-supervised machine learning, which is an emerging field in nuclear power condition monitoring, is the combination of ideas from both supervised and unsupervised machine learning whereby the data that is used to train the algorithm is a combination of labeled and unlabeled data. This paper introduces the initial research that has been undertaken in creating a semi-supervised self-training algorithm to detect the presence of graphite brick cracks and then proceeds to show that there is an improvement in the classification of graphite bricks using a semi-supervised machine learning classifier compared to supervised machine learning classifiers. This improved classification performance is encouraging as it does not require time consuming and costly human analysis to obtain extra learning information from available data.

Original languageEnglish
Title of host publication10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017
EditorsAmerican Nuclear Society
Place of PublicationRed Hook, NY
Pages77-86
Number of pages10
Volume1
ISBN (Electronic)9781510851160
Publication statusPublished - 15 Jun 2017
Event10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017 - Hyatt Regency, San Francisco, United States
Duration: 11 Jun 201715 Jun 2017
http://npic-hmit2017.org/

Conference

Conference10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017
Abbreviated titleNPIC and HMIT 2017
CountryUnited States
CitySan Francisco
Period11/06/1715/06/17
Internet address

Keywords

  • monitoring
  • self-training
  • semi-supervised learning

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  • Research Output

    • 1 Citations
    • 1 Paper

    Semi-supervised learning approach for crack detection and identification in advanced gas-cooled reactor graphite bricks

    Berry, C., West, G., McArthur, S. & Rudge, A., 4 Apr 2017, (Accepted/In press). 10 p.

    Research output: Contribution to conferencePaper

    Open Access
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  • Cite this

    Berry, C., West, G., McArthur, S., & Rudge, A. (2017). Semi-supervised learning approach for crack detection and identification in advanced gas-cooled reactor graphite bricks. In A. N. S. (Ed.), 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017 (Vol. 1, pp. 77-86).